Development of sustainable irrigation planning with multi-objective fuzzy linear programming for Ukai–Kakrapar irrigation project, Gujarat, India
Bibliographic record
Abstract
Multi-objective fuzzy linear programming (MOFLP) approach is applied with four conflicting objectives, viz maximization of net benefits, employment generation, minimization of cost of cultivation and maximization of revenue generation from municipal and industrial supplies (M and I), on a water resources project (Ukai), Gujarat, India. The results from the model are reported for the most critical year (90% exceedance probability), critical year (85% exceedance probability), normal year (75% exceedance probability), and wet year (60% exceedance probability) inflow conditions. The degree of satisfaction of the proposed MOFLP model, considering all objectives together, for wet year, normal year, critical year and most critical year are found to be 0.527, 0.515, 0.50, and 0.46 respectively; and corresponding net irrigation benefits for different inflow conditions are computed as 10 611.91 Million Rs, 10 476.67 Million Rs, 8 311.0044 Million Rs, and 6 900.051 Million Rs, respectively. The proposed MOFLP model indicated that probable inflow corresponding to 75% dependability level is marginally sufficient to meet the requirement of the study area, and water availability becomes deficit in the command area for 85% dependability inflow condition. The optimized crop areas from the model, complying with the requirement of existing flood rules, and satisfying relevant conflicting objectives would help the decision makers in sustainable management of water resources in Ukai command area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".